查看文章

Improving the flame retardancy of polymeric materials used in engineering applications is an increasingly important strategy for limiting fire hazards. However, the wide variety of flame retardant polymeric nanocomposite compositions prevents quick identification of the optimal design for a specific application. In this study, we built a flame retardancy database of more than 800 polymeric nanocomposites, including information from polymer flammability, thermal stability, and nanofiller properties. Then, we applied five machine learning algorithms to predict the flame retardancy index for different types of flame retardant polymeric nanocomposites. Among them, extreme gradient boosting regression gives the best prediction with a coefficient of determination (R²) of 0.94 and a root-mean-square error of 0.17. In addition, we studied how the physical features of polymeric nanocomposites affected flame retardancy using …